Traditional stitching methods perform poorly in complex scenes, and supervised methods face challenges due to the difficulty of annotating data. Existing unsupervised image stitching methods suffer from large model parameters and long stitching times. Therefore, a lightweight unsupervised deep learning-based image stitching framework is proposed, which consists of two stages: an unsupervised image deformation network and an unsupervised image fusion network. In the image deformation network, MobileNetV2 is used as the backbone, combined with the ECA attention mechanism module to obtain image deformation information. The image fusion module employs UNeXt as the backbone network to generate seamless stitching by identifying the seam lines in the overlapping regions of the images. The accuracy is improved by incorporating the AG module and enhancing the tokenized MLP module. Additionally, due to the lack of datasets for underwater image stitching, a real-world unsupervised underwater image stitching dataset is constructed. Comparative experiments are conducted on this dataset and the publicly available UDIS-D dataset, evaluating SIFT+Ransac, ORB+Ransac, UDIS, and UDIS++ algorithms. The experimental results demonstrate that the proposed algorithm reduces the model parameters by 74% and improves stitching speed by 46% while maintaining stitching accuracy.